Maps Dashboard

library(rvest)
library(dplyr)
library(tidyverse)
library(httr)
library(ggplot2)
library(tidyr)
library(maps)
library(knitr)
library(viridis)
library(broom)
library(plotly)  

knitr::opts_chunk$set(
  fig.width = 8,  
  fig.asp = 0.5,     
  out.width = "90%"   
)


theme_set(theme_minimal() + theme(legend.position = "right"))

options(
  ggplot2.continuous.colour = "viridis",
  ggplot2.continuous.fill = "viridis"
)

scale_colour_discrete = scale_colour_viridis_d
scale_fill_discrete = scale_fill_viridis_d

With the data available for all esports players, our analysis focuses specifically on the top 500 players based on their earnings in the last year. The goal is to explore country-level insights by aggregating data for total earnings, player counts, and average earnings per player across different countries.

# Data import
rm(list = ls())

country_overall <- read.csv("data/country_overall.csv")
esports_top100 <- read.csv("data/esports_earnings_top100_overall.csv")
top500_365 <- read.csv("data/top500_365.csv")
earnings_country <- read.csv("data/Earnings_country_2018_2024.csv")
earnings_topgame <- read.csv("data/Earnings_topgame_2018_2024.csv")

country_overall$Total_Earnings <- as.numeric(gsub("[\\$,]", "", country_overall$Total_Earnings))
esports_top100$Total_Earnings <- as.numeric(gsub("[\\$,]", "", esports_top100$Total_Earnings))
top500_365$`Total..Last.365.Days.` <- as.numeric(gsub("[\\$,]", "", top500_365$`Total..Last.365.Days.`))
earnings_country$Overall_Earnings <- as.numeric(gsub("[\\$,]", "", earnings_country$Overall_Earnings))
earnings_topgame$Total_Earnings <- as.numeric(gsub("[\\$,]", "", earnings_topgame$Total_Earnings))

#  Prepare map data
world_map <- map_data("world")

# Extract unique country names from your data
unique_countries <- unique(top500_365$Country)

# Extract unique country names from the map data
world_map <- map_data("world")
map_countries <- unique(world_map$region)

# Identify mismatches
mismatched_countries <- setdiff(unique_countries, map_countries)

# Print mismatched countries for manual inspection
# print(mismatched_countries)

top500_365$Country <- recode(top500_365$Country, 
                             "Taiwan, Republic of China" = "Taiwan",
                             "United States" = "USA",
                             "Russian Federation" = "Russia",
                             "Korea, Republic of" = "South Korea",
                             "Hong Kong" = "Hong Kong SAR China",
                             "Moldova, Republic of" = "Moldova",
                             "United Kingdom" = "UK",
                             "Iran, Islamic Republic of" = "Iran",
                             "Macao" = "Macau")

Total Earnings from top 500 players by country

# Aggregate data by country
country_summary <- top500_365 %>%
  group_by(Country) %>%
    summarise(
    Player_Count = n(),  # Count the number of players
    Total_Earnings = sum(`Total..Last.365.Days.`, na.rm = TRUE),  # Sum up earnings
    Average_Earnings = Total_Earnings / Player_Count  # Calculate average earnings per player
  )


# Merge map data with the aggregated dataset
map_data_merged <- world_map %>%
  left_join(country_summary, by = c("region" = "Country"))  # Match country names

# 绘制交互式地图
p1 <- ggplot(data = map_data_merged, 
             aes(x = long, 
                 y = lat, 
                 group = group, 
                 fill = Total_Earnings,
                 text = paste(
                   "Country: ", region,
                   "<br>Player Count: ", Player_Count
                    )
                 )
             ) +
  geom_polygon(color = "white") +
  scale_fill_viridis_c(option = "viridis", na.value = "gray90", name = "Total Earnings (USD)") +
  labs(
    title = "Total Earnings by Country (Last 365 Days)",
    x = "", y = ""
  ) +
  theme_void() +
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5))

# 转换为交互式
plotly::ggplotly(p1)

Average Earnings from top 500 players by country

# Plot the map for Average Earnings
p2 <- ggplot(data = map_data_merged, aes(x = long, y = lat, group = group, fill = Average_Earnings, text = paste(
                   "Country: ", region,
                   "<br>Player Count: ", Player_Count
                    ))) +
  geom_polygon(color = "white") +
  scale_fill_viridis_c(option = "viridis", na.value = "gray90", name = "Average Earnings (USD)") +
  labs(
    title = "Average Earnings by Country (Last 365 Days)",
    x = "", y = ""
  ) +
  theme_void() +
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5))

plotly::ggplotly(p2)

Number of players from top 500 players by country

#  Plot heatmap for Player Count with viridis colors
p3 <- ggplot(data = map_data_merged, aes(x = long, y = lat, group = group, fill = Player_Count, text = paste(
                   "Country: ", region
                    ))) +
  geom_polygon(color = "white") +
  scale_fill_viridis_c(option = "viridis", na.value = "gray90", name = "Player Count") +
  labs(
    title = "Number of Players by Country (Last 365 Days)",
    x = "", y = ""
  ) +
  theme_void() +
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5))

plotly::ggplotly(p3)